PFAS: A Resource-Performance-Fluctuation-Aware Workflow Scheduling Algorithm for Grid Computing

Resource performance in the computational grid is not only heterogeneous, but also changing dynamically. However scheduling algorithms designed for traditional parallel and distributed systems, such as clusters, only consider the heterogeneity of the resources. In this paper, a workflow scheduling algorithm, called PFAS, is proposed and tested in the grid environment. PFAS considers dynamic resource performance fluctuation in the grid, and conducts the scheduling according to its knowledge of the fluctuation. This new algorithm works in an offline way which allows it to be easily set up and run with less cost. Simulations show that our approach can achieve better schedules than the HEFT algorithm.

[1]  Justin R. Smith The Design and Analysis of Parallel Algorithms , 1993 .

[2]  Salim Hariri,et al.  Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing , 2002, IEEE Trans. Parallel Distributed Syst..

[3]  Stephen A. Jarvis,et al.  Mapping DAG-based applications to multiclusters with background workload , 2005, CCGrid 2005. IEEE International Symposium on Cluster Computing and the Grid, 2005..

[4]  Ishfaq Ahmad,et al.  Dynamic Critical-Path Scheduling: An Effective Technique for Allocating Task Graphs to Multiprocessors , 1996, IEEE Trans. Parallel Distributed Syst..

[5]  Jing-Chiou Liou,et al.  A comparison of general approaches to multiprocessor scheduling , 1997, Proceedings 11th International Parallel Processing Symposium.

[6]  Wayne H. Wolf,et al.  TGFF: task graphs for free , 1998, Proceedings of the Sixth International Workshop on Hardware/Software Codesign. (CODES/CASHE'98).

[7]  Gabriel Mateescu Quality of Service on the Grid Via Metascheduling with Resource Co-Scheduling and Co-Reservation , 2003, Int. J. High Perform. Comput. Appl..

[8]  Lingyun Yang,et al.  Conservative Scheduling: Using Predicted Variance to Improve Scheduling Decisions in Dynamic Environments , 2003, ACM/IEEE SC 2003 Conference (SC'03).

[9]  Yolanda Gil,et al.  Pegasus: Mapping Scientific Workflows onto the Grid , 2004, European Across Grids Conference.

[10]  Akshai K. Aggarwal,et al.  An adaptive generalized scheduler for grid applications , 2005, 19th International Symposium on High Performance Computing Systems and Applications (HPCS'05).

[11]  Dharma P. Agrawal,et al.  Improving scheduling of tasks in a heterogeneous environment , 2004, IEEE Transactions on Parallel and Distributed Systems.

[12]  Radu Prodan,et al.  Scheduling of scientific workflows in the ASKALON grid environment , 2005, SGMD.

[13]  Dharma P. Agrawal,et al.  Optimal Scheduling Algorithm for Distributed-Memory Machines , 1998, IEEE Trans. Parallel Distributed Syst..

[14]  Rajkumar Buyya,et al.  Critical-path and priority based algorithms for scheduling workflows with parameter sweep tasks on global grids , 2005, 17th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD'05).

[15]  Hesham El-Rewini,et al.  Parallax: a tool for parallel program scheduling , 1993, IEEE Parallel & Distributed Technology: Systems & Applications.

[16]  Dong Lu,et al.  Synthesizing Realistic Computational Grids , 2003, ACM/IEEE SC 2003 Conference (SC'03).

[17]  Rizos Sakellariou,et al.  An Experimental Investigation into the Rank Function of the Heterogeneous Earliest Finish Time Scheduling Algorithm , 2003, Euro-Par.

[18]  Hesham H. Ali,et al.  Task scheduling in parallel and distributed systems , 1994, Prentice Hall series in innovative technology.

[19]  Subhash Saini,et al.  GridFlow: workflow management for grid computing , 2003, CCGrid 2003. 3rd IEEE/ACM International Symposium on Cluster Computing and the Grid, 2003. Proceedings..

[20]  Tao Yang,et al.  DSC: Scheduling Parallel Tasks on an Unbounded Number of Processors , 1994, IEEE Trans. Parallel Distributed Syst..